The goal of this clinical trial is to see if an artificial intelligence (AI)-assisted method helps doctors more accurately detect invasive breast cancer in people with a specific type of tumor called "extensive intraductal carcinoma" (EIC). This type of tumor is challenging to diagnose correctly using standard methods. The main question this study aims to answer is: Does the new AI-assisted method find more invasive breast cancer in EIC tumors compared to the standard method?
Researchers will compare two groups:
* Group 1 (INSIGHT): Doctors review breast tissue samples using an AI tool that highlights suspicious areas needing closer attention. * Group 2 (Conventional): Doctors review breast tissue samples without AI help, using the standard method.
This comparison will show if the AI-assisted method works better at finding invasive cancer.
What happens in the study?
* Researchers will use stored breast tissue samples already collected during the participant's surgery. * Each sample will be randomly assigned to be reviewed using either the new AI-assisted method (Group 1) or the standard method (Group 2). * In Group 1, an AI program will scan the tissue images first and point out areas that might contain invasive cancer for the doctor to check closely. * In Group 2, doctors will review the tissue images without any AI help, using their standard process. * Researchers will measure which method finds invasive cancer more accurately, how long the review takes, and how many additional tests (called IHC stains) are needed.
No new procedures are required from participants; the study uses existing tissue samples.
DCIS (ductal carcinoma in situ) with or without invasive carcinoma, as confirmed by core needle biopsy prior to surgery.
Tumor size >2 cm (cT2-cT4 according to AJCC 8th edition staging) with extensive calcifications, as documented by ultrasound or MRI.
Undergone either mastectomy or breast-conserving surgery.
Histopathological examination showing DCIS comprising ≥80% of the total tumor volume in the surgical specimen.
Received neoadjuvant therapy (chemotherapy, endocrine therapy, or targeted therapy) before surgery.
History of vacuum-assisted biopsy (VAB) or other minimally invasive breast procedures that may alter tumor architecture.
Insufficient or degraded tissue samples (e.g., due to fixation artifacts, sectioning errors, or poor staining quality).
Tumors lacking a DCIS (ductal carcinoma in situ) component upon histological examination.
Parallel
INvasion Screening with Intelligent Guidance for Histopathology Triage (INSIGHT) Workflow
Other interventionAn AI-generated segmentation model are refined through a post-processing pipeline: retaining only invasive carcinoma (IC) regions, filtering detections \<500 µm², grouping adjacent IC areas, and generating per-cluster bounding boxes (red boxes). This converted raw segmentations into clinically actionable ROI proposals, balancing sensitivity and specificity for pathologist review in external testing and clinical validation. The INSIGHT workflow addresses key diagnostic challenges in EIC cases by pre-screening whole-slide images (WSIs) and intelligently marking potential IC regions. This guides pathologists to prioritize diagnostically critical areas across multiple slides or within extensive DCIS - a task particularly valuable when IC is multifocal or presents as subtle micro-invasive foci easily overlooked during routine manual examination.
Diagnostic sensitivity
Diagnostic sensitivity for invasive carcinoma detection in breast cancer with extensive intraductal component
Diagnostic efficiency
Diagnostic time cost per case (minutes)
Immunohistochemical (IHC) stains utilization
Immunohistochemical (IHC) stains utilization rate (stains/case)
Diagnostic specificity
Diagnostic specificity for invasive carcinoma detection in breast cancer with extensive intraductal component
Negative predictive value (NPV)
Negative predictive value (NPV) for invasive carcinoma detection in breast cancer with extensive intraductal component
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This trial is not recruiting at the moment. You can still explore other options:
